{"paper_id":"01aed93e-7b99-48a7-af2b-09ffe1a26f8a","body_text":"Net Present Value based Supplier Selection Problem under Carbon Emission Constraints: A Hybrid Immune-genetic Algorithm Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Net Present Value based Supplier Selection Problem under Carbon Emission Constraints: A Hybrid Immune-genetic Algorithm Approach Shamsunnahar Khanam, Md. Golam Kibria, Dewan Hafiz Nabil, Md. Limonur Rahman Lingkon, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6414967/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Supplier selection is critical to supply chain management, influencing operational efficiency, sustainability, and financial performance. This study presents a multi-criteria-based non-linear integer programming model designed to maximize discounted cash flows' net present value (NPV) while meeting carbon emissions constraints. A hybrid immune-genetic algorithm (IGA) is developed, integrating the local search diversity of immune algorithms (IA) with the fast convergence capabilities of genetic algorithms (GA). This combination enhances solution quality by mitigating premature convergence and improving search efficiency. The proposed model and IGA are tested on various problem scales, ranging from small (5 suppliers, 5 products) to large (100 suppliers, 100 products). Comparative results indicate that the IGA consistently outperforms standalone IA and GA in solution accuracy and computational efficiency. This research contributes to advancing meta-heuristic optimization techniques. It offers an effective decision-support tool for businesses to address complex supplier selection challenges while integrating sustainability considerations into supply chain practices. Supplier selection multi-criteria decision-making multi-product genetic algorithm immune algorithm immune genetic algorithm Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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